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authorBonface2024-02-09 09:41:28 -0600
committerMunyoki Kilyungi2024-08-09 13:30:43 +0300
commitd029d5d7f8ead1f1de8d318045004a4a6f68f5fb (patch)
tree33c7ff40e3f953d030ed08f468f7afb1dfcba9e6 /general/datasets/CMS_Hipp_ZScr_1115/processing.rtf
parent769ff7825f5d8d36d541e90534c07f1985899973 (diff)
downloadgn-docs-d029d5d7f8ead1f1de8d318045004a4a6f68f5fb.tar.gz
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+<p><strong>Outlier Detection.&nbsp;</strong>Samples 7 (J-CR-21), 24 (N-R-29), and 40 (D-R-6) were detected as outliers and have abnormal expression profiles (i.e. they do not cluster with other samples and have abnormal median and quartile ranges after normalization. These samples have been removed from the analysis.</p>
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+<p><strong>RMA Algorithm.&nbsp;</strong>The Robust Multichip Analysis (RMA) algorithm fits a robust linear model at the probe level to minimize the effect of probe-specific affinity differences. This approach: n Increases sensitivity to small changes between experiment and control samples. n Minimizes variance across the dynamic range, but does compress calculated fold change values. RMA consists of three steps: 1. Background adjustment 2. Quantile normalization 3. Summarization This is a multi-chip analysis approach. Therefore, all arrays intended for comparison should be included together in the summarization step. For a more detailed description of the RMA algorithm, see the publication, Exploration, Normalization, and Summaries of High Density Oligonucleotide Array Probe Level Data, Biostatistics, April 2003; Vol. 4; Number 2: 249&ndash;264.</p>